Graphical workflow system for modification calling by machine learning of reverse transcription signatures

Autoren

Lukas Schmidt
Stephan Werner
Thomas Kemmer
Stefan Niebler
Marco Kristen
Lilia Ayadi
Patrick Johe
Virginie Marchand
Tanja Schirmeister
Yuri Motorin
Andreas Hildebrandt
Bertil Schmidt
Mark Helm

DOI

10.3389/fgene.2019.00876

eISSN

1664-8021

Zeitschrift

Frontiers in Genetics

Online publication date

2019

Status

Published online

Herausgeber

Frontiers Media SA

Herausgeber URL

http://dx.doi.org/10.3389/fgene.2019.00876

Datum der Datenerfassung

2019

Titel

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

Ausgabe der Zeitschrift

10

Data source: Crossref

Abstract

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.

Addresses

Institute of Pharmacy and Biochemistry, Johannes Gutenberg-University, Mainz, Germany.

Autoren

Lukas Schmidt
Stephan Werner
Thomas Kemmer
Stefan Niebler
Marco Kristen
Lilia Ayadi
Patrick Johe
Virginie Marchand
Tanja Schirmeister
Yuri Motorin
Andreas Hildebrandt
Bertil Schmidt
Mark Helm

DOI

10.3389/fgene.2019.00876

eISSN

1664-8021

Externe Identifier

PubMed Identifier: 31608115
PubMed Central ID: PMC6774277

Funding acknowledgements

EU Joint Programme – Neurodegenerative Disease Research: RNA Neuro
Deutsche Forschungsgemeinschaft: HE3397/13-2, RO 4681/6-1
Bundesministerium für Bildung und Forschung: FKZ: 01ED1804

Open access

true

ISSN

1664-8021

Zeitschrift

Frontiers in genetics

Sprache

eng

Medium

Electronic-eCollection

Online publication date

2019

Open access status

Open Access

Paginierung

876

Datum der Veröffentlichung

2019

Status

Published

Publisher licence

CC BY

Datum der Datenerfassung

2019

Titel

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Sub types

research-article
Journal Article

Ausgabe der Zeitschrift

10

Files

https://www.frontiersin.org/articles/10.3389/fgene.2019.00876/pdf https://europepmc.org/articles/PMC6774277?pdf=render

Data source: Europe PubMed Central

Abstract

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.

Date of acceptance

2019

Autoren

Lukas Schmidt
Stephan Werner
Thomas Kemmer
Stefan Niebler
Marco Kristen
Lilia Ayadi
Patrick Johe
Virginie Marchand
Tanja Schirmeister
Yuri Motorin
Andreas Hildebrandt
Bertil Schmidt
Mark Helm

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/31608115

DOI

10.3389/fgene.2019.00876

Externe Identifier

PubMed Central ID: PMC6774277

ISSN

1664-8021

Zeitschrift

Front Genet

Schlüsselwörter

Galaxy platform
RNA modifications
RT signature
Watson–Crick face
m1A
machine learning

Sprache

eng

Country

Switzerland

Paginierung

876

Datum der Veröffentlichung

2019

Status

Published online

Titel

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Sub types

Journal Article

Ausgabe der Zeitschrift

10

Data source: PubMed

Autoren

Lukas Schmidt
Stephan Werner
Thomas Kemmer
Stefan Niebler
Lilia Ayadi
Patrick Johe
Virginie Marchand
Tanja Schirmeister
Yuri MOTORIN
Andreas Hildebrandt
others

Zeitschrift

Frontiers in genetics

Paginierung

876 - 876

Datum der Veröffentlichung

2019

Herausgeber

Frontiers

Datum der Datenerfassung

2020

Titel

Graphical workflow system for modification calling by machine learning of reverse transcription signatures

Sub types

article

Ausgabe der Zeitschrift

10

Data source: Manual

Graphical workflow system for modification calling by machine learning of reverse transcription signatures

Files

Tools